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as well as the Canadian Safety and Security Program, CFS and Defence Research and Development Canada","award":["NE\/Ro16518\/1"],"award-info":[{"award-number":["NE\/Ro16518\/1"]}]},{"name":"AFFES and the Canadian Forest Service (CFS), as well as the Canadian Safety and Security Program, CFS and Defence Research and Development Canada","award":["RC-2018-023"],"award-info":[{"award-number":["RC-2018-023"]}]},{"name":"AFFES and the Canadian Forest Service (CFS), as well as the Canadian Safety and Security Program, CFS and Defence Research and Development Canada","award":["CSSP-2019-TI-2442"],"award-info":[{"award-number":["CSSP-2019-TI-2442"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Wildfire research is working toward near real-time tactical wildfire mapping through the application of computer vision techniques to airborne thermal infrared (IR) imagery. One issue hindering automation is the potential for waterbodies to be marked as areas of combustion due to their relative warmth in nighttime thermal imagery. Segmentation and masking of waterbodies could help resolve this issue, but the reliance on data captured exclusively in the thermal IR and the presence of real areas of combustion in some of the images introduces unique challenges. This study explores the use of the random forest (RF) classifier for the segmentation of waterbodies in thermal IR images containing a heterogenous wildfire. Features for classification are generated through the application of contextual and textural filters, as well as normalization techniques. The classifier\u2019s outputs are compared against static GIS-based data on waterbody extent as well as the outputs of two unsupervised segmentation techniques, based on entropy and variance, respectively. Our results show that the RF classifier achieves very high balanced accuracy (&gt;98.6%) for thermal imagery with and without wildfire pixels, with an overall F1 score of 0.98. The RF method surpassed the accuracy of all others tested, even with heterogenous training sets as small as 20 images. In addition to assisting automation of wildfire mapping, the efficiency and accuracy of this approach to segmentation can facilitate the creation of larger training data sets, which are necessary for invoking more complex deep learning approaches.<\/jats:p>","DOI":"10.3390\/rs14092262","type":"journal-article","created":{"date-parts":[[2022,5,8]],"date-time":"2022-05-08T23:27:25Z","timestamp":1652052445000},"page":"2262","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Machine Learning Approach to Waterbody Segmentation in Thermal Infrared Imagery in Support of Tactical Wildfire Mapping"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9209-4304","authenticated-orcid":false,"given":"Jacqueline A.","family":"Oliver","sequence":"first","affiliation":[{"name":"Faculty of Science and Technology, Athabasca University, Athabasca, AB T9S 3A3, Canada"},{"name":"Northern Forestry Centre, Canadian Forest Service, Natural Resources Canada, 5320-122nd Street, Edmonton, AB T6H 3S5, Canada"}]},{"given":"Fr\u00e9d\u00e9rique C.","family":"Pivot","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, Athabasca University, Athabasca, AB T9S 3A3, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6447-2133","authenticated-orcid":false,"given":"Qing","family":"Tan","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, Athabasca University, Athabasca, AB T9S 3A3, Canada"}]},{"given":"Alan S.","family":"Cantin","sequence":"additional","affiliation":[{"name":"Great Lakes Forestry Centre, Canadian Forest Service, Natural Resources Canada, 1219 Queen St. E., Sault Ste. Marie, ON P6A 2E5, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6375-7949","authenticated-orcid":false,"given":"Martin J.","family":"Wooster","sequence":"additional","affiliation":[{"name":"Leverhulme Center for Wildfires, Environment and Society, NERC National Centre for Earth Observation, Department of Geography, King\u2019s College London, London WC2B 4BG, UK"}]},{"given":"Joshua M.","family":"Johnston","sequence":"additional","affiliation":[{"name":"Great Lakes Forestry Centre, Canadian Forest Service, Natural Resources Canada, 1219 Queen St. E., Sault Ste. Marie, ON P6A 2E5, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.rse.2018.08.005","article-title":"The Collection 6 MODIS Burned Area Mapping Algorithm and Product","volume":"217","author":"Giglio","year":"2018","journal-title":"Remote Sens. 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